OpenAI Researcher Dan Roberts on What Physics Can Teach Us About AI

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#Tech #Business #Sequoia Capital

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Squid Game is back—and this time, the knives are out. In the thrilling Season 3 premiere, Player 456 is spiraling and a brutal round of hide-and-seek forces players to kill or be killed. Hosts Phil Yu and Kiera Please break down Gi-hun’s descent into vengeance, Guard 011’s daring betrayal of the Game, and the shocking moment players are forced to choose between murdering their friends… or dying. Then, Carlos Juico and Gavin Ruta from the Jumpers Jump podcast join us to unpack their wild theories for the season. Plus, Phil and Kiera face off in a high-stakes round of “Hot Sweet Potato.” SPOILER ALERT! Make sure you watch Squid Game Season 3 Episode 1 before listening on. Play one last time. IG - @SquidGameNetflix X (f.k.a. Twitter) - @SquidGame Check out more from Phil Yu @angryasianman , Kiera Please @kieraplease and the Jumpers Jump podcast Listen to more from Netflix Podcasts . Squid Game: The Official Podcast is produced by Netflix and The Mash-Up Americans.…

about a year ago 41:42

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In recent years there’s been an influx of theoretical physicists into the leading AI labs. Do they have unique capabilities suited to studying large models or is it just herd behavior? To find out, we talked to our former AI Fellow (and now OpenAI researcher) Dan Roberts.

Roberts, co-author of The Principles of Deep Learning Theory, is at the forefront of research that applies the tools of theoretical physics to another type of large complex system, deep neural networks. Dan believes that DLLs, and eventually LLMs, are interpretable in the same way a large collection of atoms is—at the system level. He also thinks that emphasis on scaling laws will balance with new ideas and architectures over time as scaling asymptotes economically.

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital

Mentioned in this episode:

The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks, by Daniel A. Roberts, Sho Yaida, Boris Hanin
Black Holes and the Intelligence Explosion: Extreme scenarios of AI focus on what is logically possible rather than what is physically possible. What does physics have to say about AI risk?
Yang-Mills & The Mass Gap: An unsolved Millennium Prize problem

AI Math Olympiad: Dan is on the prize committee

55 episodes

OpenAI Researcher Dan Roberts on What Physics Can Teach Us About AI

Training Data

16 subscribers

published about a year ago

MP3•Episode home

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital

Mentioned in this episode:

The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks, by Daniel A. Roberts, Sho Yaida, Boris Hanin
Black Holes and the Intelligence Explosion: Extreme scenarios of AI focus on what is logically possible rather than what is physically possible. What does physics have to say about AI risk?
Yang-Mills & The Mass Gap: An unsolved Millennium Prize problem

AI Math Olympiad: Dan is on the prize committee

55 episodes

#Tech #Business #Sequoia Capital

All episodes

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1
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